Prediction of Thermogravimetric Data in the Thermal Recycling of e-waste Using Machine Learning Techniques: A...

The release of bromine-free hydrocarbons and gases is a major challenge faced in the thermal recycling of e-waste due to the corrosive effects of produced HBr. Metal oxides such as Fe₂O₃ (hematite) are excellent debrominating agents, and they are copyrolyzed along with tetrabromophenol (TBP), a lesser used brominated flame retardant that is a constituent of printed circuit boards in electronic equipment. The pyrolytic (N₂) and oxidative (O₂) decomposition of TBP with Fe₂O₃ has been previously investigated with thermogravimetric analysis (TGA) at four different heating rates of 5, 10, 15, and 20 °C/min, and the mass loss data between room temperature and 800 °C were reported. The objective of our paper is to study the effectiveness of machine learning (ML) techniques to reproduce these TGA data so that the use of the instrument can be eliminated to enhance the potential of online monitoring of copyrolysis in e-waste treatment. This will reduce experimental and human errors as well as improve process time significantly. TGA data are both nonlinear and multidimensional, and hence, nonlinear regression techniques such as random forest (RF) and gradient boosting regression (GBR) showed the highest prediction accuracies of 0.999 and lowest prediction errors among all the ML models employed in this work. The large data sets allowed us to explore three different scenarios of model training and validation, where the number of training samples were varied from 10,000 to 40,000 for both TBP and TBP + hematite samples under N₂ (pyrolysis) and O₂ (combustion) environments. The novelty of our study is that ML techniques have not been employed for the copyrolysis of these compounds, while the significance is the excellent potential of enhanced online monitoring of e-waste treatment and extension to other characterization techniques such as spectroscopy and chromatography. Lastly, e-waste recycling could greatly benefit from ML applications since it has the potential to reduce total and operational costs and improve overall process time and efficiency, thereby encouraging more treatment plants to adopt these techniques, resulting in reducing the increasing environmental footprint of e-waste.